基于经验模态分解的本征熵及其应用研究

以经验模态分解和信息熵理论为基础,可以提出本征熵这一新指标,用于大型设备运行稳定性定量监测。通过在十余家大型企业的实际应用和对应实验表明,本征熵较好地反映了设备运行时不同本征模态的稳定性,为设备的状态监测和诊断提供了运行稳定性的量化数据。     

Robust image watermarking based on multiband wavelets and empirical mode decomposition.

In this paper, we propose a blind image watermarking algorithm based on the multiband wavelet transformation and the empirical mode decomposition. Unlike the watermark algorithms based on the traditional two-band wavelet transform, where the watermark bits are embedded directly on the wavelet coefficients, in the proposed scheme, we embed the watermark bits in the mean trend of some middle-frequency subimages in the wavelet domain. We further select appropriate dilation factor and filters in the multiband wavelet transform to achieve better performance in terms of perceptually invisibility and the robustness of the watermark. The experimental results show that the proposed blind watermarking scheme is robust against JPEG compression, Gaussian noise, salt and pepper noise, median filtering, and ConvFilter attacks. The comparison analysis demonstrate that our scheme has better performance than the watermarking schemes reported recently.     

Multivariate empirical mode decomposition and application to multichannel filtering

Empirical Mode Decomposition (EMD) is an emerging topic in signal processing research, applied in various practical fields due in particular to its data-driven filter bank properties. In this paper, a novel EMD approach called X-EMD (eXtended-EMD) is proposed, which allows for a straightforward decomposition of mono- and multivariate signals without any change in the core of the algorithm. Qualitative results illustrate the good behavior of the proposed algorithm whatever the signal dimension is. Moreover, a comparative study of X-EMD with classical mono- and multivariate methods is presented and shows its competitiveness. Besides, we show that X-EMD extends the filter bank properties enjoyed by monovariate EMD to the case of multivariate EMD. Finally, a practical application on multichannel sleep recording is presented.     

Reduced reference image quality assessment based on statistics in empirical mode decomposition domain

This paper deals with the image quality assessment (IQA) task using a natural image statistics approach. A reduced reference (RRIQA) measure based on the bidimensional empirical mode decomposition is introduced. First, we decompose both, reference and distorted images, into intrinsic mode functions (IMF) and then we use the generalized Gaussian density (GGD) to model IMF coefficients of the reference image. Finally, we measure the impairment of a distorted image by fitting error between the IMF coefficients histogram of the distorted image and the estimated IMF coefficients distribution of the reference image, using the Kullback–Leibler divergence (KLD). Furthermore, to predict the quality, we propose a new support vector machine-based (SVM) classification approach as an alternative to logistic function-based regression. In order to validate the proposed measure, three benchmark datasets are involved in our experiments. Results demonstrate that the proposed metric compare favorably with alternative solutions for a wide range of degradation encountered in practical situations.     

Hierarchical decomposition based on a variation of empirical mode decomposition

Adaptive methods of signal analysis have proved a very useful tool for analysis of non-stationary signals. This is due to the ability of these methods to adapt to the local structures of the signals being analysed, as these methods are not constrained by a fixed basis. Empirical mode decomposition (EMD) is among the more recent data-adaptive signal decomposition methods, which decomposes a given signal into modes which are hierarchically arranged based on their frequency content. In this paper, we will present a novel adaptive hierarchical decomposition scheme based on a novel modification of EMD, namely empirical mode decomposition-modified peak selection (EMD-MPS). EMD-MPS allows a time-scale-based signal decomposition, thereby allowing control over the decomposition process, not possible in the original EMD algorithm. Using time-scale-based decomposition and the properties of EMD-MPS, a given signal can be decomposed into octave frequency bands, with the centre frequency of the separated modes given by the frequency separation criterion of EMD-MPS. The spectral limits of the separated bands are established, and their relation with the centre frequency derived empirically. The method is validated by its application to simulated and real signals.     

Automatic analysis of human posture equilibrium using empirical mode decomposition

The present study proposes a new approach for the assessment of the human balance control. This approach is based on the decomposition of the center of pressure displacement using empirical mode decomposition (EMD) that provides an effective time-frequency analysis of non-stationary signals. Twenty-eight healthy subjects performed quiet standing in four conditions—feet apart/together with respect to eyes open/closed—while recording the stabilometric signals in the anteroposterior (AP) and mediolateral (ML) directions. The EMD method decomposes each stabilometric signal into several subsignals called intrinsic mode functions (IMFs). Stabilogram-diffusion analysis technique is applied to generate the diffusion curve of each IMF signal. Each diffusion curve is modeled as a second-order system and provides representative features, such as the gain parameter. Analysis of the gain parameter shows the major effect of visual input and feet conditions on the strategy to control/stabilize the balance. Significant differences were found between young and elderly, and between women and men. In addition, the impact of feet position seems to be higher in ML direction than in AP direction.     

The complex bidimensional empirical mode decomposition

A new method for computing complex bidimensional empirical mode decomposition (BEMD) is presented in this paper. The proposed complex-BEMD uses four quadrant spectra to apply standard BEMD to four real-valued 2D signals. The so-generated intrinsic mode functions (IMFs) are 2D complex-valued, which facilitates the extension of the standard BEMD to the complex domain. The proposed complex-BEMD can be successful for the analysis of real-world 2D complex-valued signals, such as 2D NMR signals. Moreover, the proposed complex-BEMD can be applied for color image processing. A simple color image fusion algorithm based upon the proposed complex-BEMD has also been developed to have the exhibition of the potential. By our proposed complex-BEMD and image fusion algorithm, the well-fused results can be obtained, if the mode mixing in BEMD is alleviated.     

基于经验模态分解的激光陀螺随机信号消噪

各种随机噪声是导致激光陀螺产生误差的主要因素,且其性质特殊,很难用传统的滤波方法去除。为了抑制激光陀螺的随机漂移,提高使用精度,提出了一种新型经验模态分解方法对陀螺随机漂移测试信号进行滤波处理。该方法将经验模态分解的内模函数中两个相邻过零点之间的信号定义为模态单元,并作为基本分析对象,通过对模态单元振幅的阈值处理来判断模态单元的类型,进而建立模态单元滤波模型。分析了经验模态分解法在分解不同Hurst指数分形高斯噪声时模态振幅的演化规律,并建立了一种用于高斯消噪的阈值选取规则。运用该方法对激光陀螺测试数据进行了滤波降噪实验,并用Allan方差法对不同降噪算法的降噪效果进行了比较分析,实验结果验证了该方法的有效性和优越性。     

基于经验模式分解的Wigner-Ville分布交叉项抑制方法

时频分析作为时变非平稳信号分析的有力工具,成为现代信号处理研究的一个热点.这种分析方法提供了时间域与频率域的联合分布信息,为我们清楚地描述了信号随时间变化的关系.Wigner-Ville分布由于其良好时频集聚性,在非平稳信号分析中得到广泛应用,本文针对Wigner-Ville分布中的交叉项问题,提出了基于经验模式分解的Wigner-Ville分布,即对多分量信号运用经验模式分解,将其分解为单分量信号,再对每个单分量信号求Wigner-Ville分布进行线性叠加.提出运用相关系数法对经验模式分解伪分量进行剔除,提高了该方法的精度,并将该方法与Cohen类方法进行比较,阐述了该方法的优点.     

Method for eliminating mode mixing of empirical mode decomposition based on the revised blind source separation

Since mode mixing of empirical mode decomposition (EMD) is mainly caused by the intermittence and noise, we propose a novel method to eliminate mode mixing of EMD based on the revised blind source separation. To this aim, an optimal morphological filter is employed to eliminate the noise. As a result, the component of mode mixing caused by noise is suppressed. Furthermore, the de-noised signal is decomposed into different intrinsic mode function (IMF) components through the EMD algorithm. Since it is impossible to apply blind source separation to a single channel signal directly, the IMF component, which has mode mixing is chosen and reconstructed in the phase space. Following that, the equivalent hypothetical signals are obtained. Finally, an improved fixed-point algorithm based on independent component analysis (ICA) is introduced to separate the overlapping components. The analysis of simulation and practical application demonstrates that our proposed method can effectively tackle the mode mixing problem of EMD.     

A 3-D integral equation-based approach to the analysis of real-lifeMMICs application to microelectromechanical systems

In this paper, we introduce a three-dimensional method-of-moments approach, suitable for the analysis of real-life monolithic circuits for microwave/millimeter waves. It shares the flexibility and the efficiency of the currently available spectral-domain commercial simulators, while allowing all metallizations to have finite thickness and finite conductivity and the ability to handle dielectric discontinuities. The method is successfully applied to several structures, like metal-insulator-metal capacitor, spiral inductors, and microelectromechanical capacitive switches in the 1-50-GHz frequency range     

Enhancing Poisson's equation-based approach for DCT prediction.

Yamatani and Saito recently published an interesting method for predicting discrete cosine transform (DCT) coefficients of an image block, which uses partial derivatives of the image at the block boundary points. It estimates partial derivatives the same way for all four side boundary points. In this correspondence, we improve their estimation method for the left and top side boundary points by observing that the decoder can use 1-D DCT of the rightmost column of pixels of the block on the left side and bottom row pixels of the block on the top side instead of using just the DC of these two blocks. It led us to revise their prediction equations. Experimental results show that the cumulative reduction in the size of the first five AC coefficients obtained using their equations is 15.1%, and the same using our equations is 24.6%.     

一种基于偏微分方程的图像分割算法

针对几何活动轮廓模型（GAC模型）在基于偏微分方程的图像分割领域中,算法复杂,计算量大导致演化时间长,演化速度在边界上通常不为零,引起演化曲线进入到目标的内部;或是当图像的对象有较深的凹陷边界时,曲线停在某一局部极小值状态,并不与对象的边界相一致等问题。本文提出了一种基于偏微分方程的图像分割算法,通过对停止速度场进行多尺度张量扩散,然后运用GACA模型进行分割。实验证明：本算法在不降低射线图像分割质量的前提下,可使演化时间比传统的GAC模型演化时间减少65%左右,还在一定程度上减少了边界泄露问题。     

基于经验模态分解的管道超声回波信号识别

在管道超声无损检测中,超声回波信号常受到噪声干扰或者因管道壁缺陷原因导致回波波形重叠,故检测信号很难判断缺陷大小。现采用一种基于经验模态分解(Empirical Model Decomposition,EMD)的方法,对超声回波信号的到达时间进行准确识别。该方法首先把原始采集的超声回波信号进行经验模态分解,预处理回波信号中的噪声干扰,处理后的信号通过正交检波处理得到该信号的峰值包络,则该包络的峰值时刻对应了回波信号的到达时刻。通过对实测缺陷信号的试验,验证了该方法的可行性和准确性。     

Estimation of instantaneous frequencies using iterative empirical mode decomposition

A novel instantaneous frequency-based time–frequency representation is proposed for the analysis of multicomponent signals. The concept of frequency translation is innovatively combined with the empirical mode decomposition algorithm to formulate an iterative procedure, referred to as the iterative empirical mode decomposition, to separate the components present in a signal at a suitably selected frequency resolution. The instantaneous frequency and amplitude estimated on the separated components are used to form the new time–frequency representation. The iterative empirical mode decomposition is assessed for component resolvability, and the performance of the aforementioned time–frequency representation is compared with several other time–frequency representations based on visual inspection and using objective criteria. The Hilbert spectrum formed using the iterative empirical mode decomposition not only provides high concentration of energy about the components’ instantaneous frequencies at high signal-to-noise ratio, but also good resolution while keeping the interference terms at a minimum.     

Seizure onset detection based on frequency domain metric of empirical mode decomposition

This paper explores the data-driven properties of the empirical mode decomposition (EMD) for detection of epileptic seizures. A new method in frequency domain is presented to analyze intrinsic mode functions (IMFs) decomposed by EMD. They are used to determine whether the electroencephalogram (EEG) recordings contain seizure or not. Energy levels of the IMFs are extracted as threshold level to detect the changes caused by seizure activity. A scalar value energy resulting from the energy levels is individually used as an indicator of the epileptic EEG without the requirements of multidimensional feature vector and complex machine learning algorithms. The proposed methods are tested on different EEG recordings to evaluate the effectiveness of the proposed method and yield accuracy rate up to 97.89%.     

Combination of canonical correlation analysis and empirical mode decomposition applied to denoising the labor electrohysterogram

The electrohysterogram (EHG) is often corrupted by electronic and electromagnetic noise as well as movement artifacts, skeletal electromyogram, and ECGs from both mother and fetus. The interfering signals are sporadic and/or have spectra overlapping the spectra of the signals of interest rendering classical filtering ineffective. In the absence of efficient methods for denoising the monopolar EHG signal, bipolar methods are usually used. In this paper, we propose a novel combination of blind source separation using canonical correlation analysis (BSS_CCA) and empirical mode decomposition (EMD) methods to denoise monopolar EHG. We first extract the uterine bursts by using BSS_CCA then the biggest part of any residual noise is removed from the bursts by EMD. Our algorithm, called CCA_EMD, was compared with wavelet filtering and independent component analysis. We also compared CCA_EMD with the corresponding bipolar signals to demonstrate that the new method gives signals that have not been degraded by the new method. The proposed method successfully removed artifacts from the signal without altering the underlying uterine activity as observed by bipolar methods. The CCA_EMD algorithm performed considerably better than the comparison methods.     

A robust method for parameter estimation of AR systems using empirical mode decomposition

This paper presents a robust algorithm for parameter estimation of autoregressive (AR) systems in noise using empirical mode decomposition (EMD) method. The basic idea is to represent the autocorrelation function of the noise-free AR signal as the summation of damped sinusoidal functions and use EMD for extracting these component functions as intrinsic mode functions (IMFs). Unlike conventional correlation-based techniques, the proposed scheme first estimates the damped sinusoidal model parameters from the IMFs of autocorrelation function using a least-squares based method. The AR parameters are then directly obtained from the extracted sinusoidal model parameters. Simulation results show that EMD is a very promising tool for AR system identification at a very low signal-to-noise ratio (SNR).     

极值斜率经验模式分解结合独立分量分析的单通道盲分离

针对单通道盲分离领域先验信息不足的问题,提出了一种基于极值斜率经验模式分解和独立分量分析的单通道盲分离算法。首先通过极值斜率经验模式分解算法以不同尺度逐次分解混合信号的波动和趋势,得到一组固有模态函数。然后将其作为独立分量分析算法的输入信号,从得到的独立分量中萃取出与源信号相似的独立分量,通过重构算法恢复源信号,达到分离目的。实验信号采取仿真信号和实际信号,实验结果表明,该算法不需任何先验信息,鲁棒性强,能较快地得到良好的分离效果。     

经验模式分解用于激光雷达大气回波处理

为了提高激光大气回波处理的时效性,采用了经验模式分解方法,分析了经验模式分解的原理,研究了经验模式分解在激光雷达大气回波信号实时处理中的应用.进行了理论分析与仿真验证,取得了经验模式分解和100次信号平均处理后的结果,并进行了比较.结果表明,经验模式分解处理后的重构信号与100次信号平均结果接近,相关系数为0.99,同时处理时间缩短为1%,可以满足某些情况下的实时处理要求,并且能够抑制回波中一些薄云的干扰.这一结果对激光雷达大气回波信号实时处理是有帮助的.